optpart (version 3.0-3)

optindval: Optimizing Classification by Maximizing Dufrene and Legendre's Indicator Value

Description

optindval is a iterative re-assignment classification algorithm that assigns samples to clusters to maximize the sum of indicator values.

Usage

optindval(comm,clustering,maxitr=100,minsiz=5)

Arguments

comm

a vegetation or other taxon table with samples as rows and taxa as columns

clustering

an index of cluster membership for each sample. May be either a numeric vector of length equal to the number of samples, or an object that inherits from class ‘cluster’

maxitr

the maximum number of iterations to attempt

minsiz

the minimum size cluster to consider reassigning a sample out of

Value

a list of class "optindval","clustering" with components:

numitr

the number of iterations performed

sums

a vector of indicator value probability sums

clustering

the vector of cluster memberships (as integers) for each sample

Details

Iterative re-allocation algorithms temporarily re-assign each sample to each of the other possible clusters and calculate a goodness-of-clustering statistic for each re-assignment. The best of all possible re-assignments is then executed and the algorithm iterates until there are no more good re-assignments or the maximum number of iterations is reached. In optindval, the goodness-of-clustering statistic is the sum of Dufrene and Legendre indicator values

References

http://ecology.msu.montana.edu/labdsv/R

See Also

optpart, opttdev, optsil

Examples

# NOT RUN {
data(shoshveg) # returns a data.frame of vegetation data called shoshveg
dis.bc <- dsvdis(shoshveg,'bray') # generate Bray/Curtis dissimilarity 
                                  # matrix
opt.5 <- optpart(5,dis.bc) # generate 5-cluster optpart
# }
# NOT RUN {
res <- optindval(shoshveg,opt.5)
# }
# NOT RUN {
 # polish the optpart result
# }
# NOT RUN {
classmatch(opt.5,res)
# }
# NOT RUN {
 # see the plot re-assignments
# }